Search Results (14)

The Internet is not just used for communication, transactions, and cloud storage; it also serves as a massive knowledge store where both people and machines can create, analyze, and use data and information. The Semantic Web was designed to enable machines to interpret the meaning of data, facilitating more informed and autonomous decision-making. The foundation of the Semantic Web is the Resource Description Framework (RDF). The standard RDF is limited to representing simple binary relationships in the form of the $<subjectpredicateobject>$ triple. In this paper, we present a new data model called BiTemporal RDF (BiTRDF), which adds valid time and transaction time to the standard RDF. Our approach treats temporal information as references instead of attributes, simplifying the semantics while enhancing the model’s expressiveness and consistency. BiTRDF treats all resources and relationships as inherently bitemporal, enabling the representation and reasoning of complex temporal relationships in RDF. Illustrative examples demonstrate the model’s support for type propagation, domain-range inference, and transitive relationships in a temporal setting. While this work lays a theoretical foundation, future research will address implementation, query language support, and compatibility with RDF streams and legacy systems. Full article

Ensuring the trustworthiness of data used in real-time analytics remains a critical challenge in smart city monitoring and decision-making. This is because the traditional data validation methods are insufficient for handling the dynamic and heterogeneous nature of Internet of Things (IoT) data streams. This paper describes a semantic IoT streaming data validation approach to provide a semantic IoT data model and process IoT streaming data with the semantic stream processing systems to check the quality requirements of IoT streams. The proposed approach enhances the understanding of smart city data while supporting real-time, data-driven decision-making and monitoring processes. A publicly available sensor dataset collected from a busy road in Milan city is constructed, annotated and semantically processed by the proposed approach and its architecture. The architecture, built on a robust semantic-based system, incorporates a reasoning technique based on forward rules, which is integrated within the semantic stream query processing system. It employs serialized Resource Description Framework (RDF) data formats to enhance stream expressiveness and enables the real-time validation of missing and inconsistent data streams within continuous sliding-window operations. The effectiveness of the approach is validated by deploying multiple RDF stream instances to the architecture before evaluating its accuracy and performance (in terms of reasoning time). The approach underscores the capability of semantic technology in sustaining the validation of IoT streaming data by accurately identifying up to 99% of inconsistent and incomplete streams in each streaming window. Also, it can maintain the performance of the semantic reasoning process in near real time. The approach provides an enhancement to data quality and credibility, capable of providing near-real-time decision support mechanisms for critical smart city applications, and facilitates accurate situational awareness across both the application and operational levels of the smart city. Full article

This study explores the co-pelletization of sludge with landfill-mined plastic waste as a method to create high-energy refuse-derived fuel (RDF), addressing both plastic and sludge waste streams. Key variables used in RDF pelletization included sludge-to-plastic mixing ratios (50:50, 75:25, and 100:0 wt%), mold temperatures (100 °C and 120 °C), and compression pressures (60–80 MPa). Results showed that the characteristics of pellets improved considerably as the mass percentage of plastic waste increased. The 75% sludge mixture produced pellets with high compressive strength (15.9–16.4 MPa), indicating rigid and ductile properties, and achieved a calorific value of up to 33.4 MJ/kg. Mercury levels of the RDF (0.02–0.04 mg/MJ) met solid recovered fuel standards. However, the elevated chlorine content (>3 wt%_db) highlighted the necessity of removing PVC from the plastic waste before pelletization. Carbon emission factors for the pellets (23–25 kg CO₂/GJ) were comparable to commercial RDFs and notably lower than coal, demonstrating their potential as a sustainable alternative fuel source. An assessment of the entire production and utilization chain, including sludge drying, plastic sorting, pelletization, and combustion, revealed that co-pelletization reduces greenhouse gas emissions by more than 24.3% compared to current practices. Full article

Over the years, RDF streaming has been explored in research and practice from many angles, resulting in a wide range of RDF stream definitions. This variety presents a major challenge in discussing and integrating streaming systems due to a lack of a common language. This work attempts to address this critical research gap by systematizing RDF stream types present in the literature in a novel taxonomy. The proposed RDF Stream Taxonomy (RDF-STaX) is embodied in an OWL 2 DL ontology that follows the FAIR principles, making it readily applicable in practice. Extensive documentation and additional resources are provided to foster the adoption of the ontology. Three use cases for the ontology are presented with accompanying competency questions, demonstrating the usefulness of the resource. Additionally, this work introduces a novel nanopublications dataset, which serves as a collaborative, living state-of-the-art review of RDF streaming. The results of a multifaceted evaluation of the resource are presented, testing its logical validity, use case coverage, and adherence to the community’s best practices, while also comparing it to other works. RDF-STaX is expected to help drive innovation in RDF streaming by fostering scientific discussion, cooperation, and tool interoperability. Full article

This study characterises materials that belong to the group of refuse-derived fuels (RDF). This group of materials regarded as an alternative fuel is derived from industrial, municipal solid and commercial wastes. The aim of this study is to evaluate the quality of waste composition, demonstrate statistically different values and the energy efficiency of the fuel derived from waste. Data on incinerated waste were collected from two different sources. The basic physical and chemical parameters of waste include density and water content. The lower heating value (LHV) of waste, chlorine concentration and ash content of two groups of incinerated waste were also evaluated and compared for a given period of time (one year, with monthly breakdown). Statistical analysis indicated the differences in the combustion of waste groups, visualized by box plots and other diagrams to show the distribution of the results. An analysis of exhaust gas parameters was carried out, both in terms of chemical composition and energy parameters. The RDF combustion process was presented through simulations for the adopted conditions of heat recovery. It was found that for each kilogram of RDF, about 3.85 kWh (13,860 kJ) of heat can be obtained. The combustion process was simulated using Aspen Plus software. Full article

The organic fraction of municipal solid waste (OFMSW) and refuse-derived fuel (RDF) mainly consisting of paper/cardboard can be used as feedstock for the production of cellulosic ethanol. In this paper, an efficient technology is described to convert waste paper/cardboard into cellulosic ethanol. The process involves separation of the OF from the other components in the waste stream. An acid pretreatment is used to liberate the cellulosic fibers and the accessibility of the enzyme Cellic CTEC3 loading 3.75–11.25 FPU/g paper in a fed-batch addition up to 22.5% solid yield, 15 g sugars/l with a saccharification yield up to 90%. A semi-simultaneous fermentation process (SSFP) with a saccharomyces cerevisae strain MDS130 capable of fermenting both pentoses and hexoses are growing an ethanol titer (%v/v) of 8.4% on pilon-plant scale. Full article

Nutrient recovery from biomass streams generates novel recycling-derived fertilizers (RDFs). The effect of RDFs depends on their nutrient content and variability, which can aid or hinder their use by end-users. Detailed characterization of RDFs can help in evaluating product properties, whereas blending RDFs can optimize their nutrient ratios and reduce nutrient variability. This study assesses ammonium nitrate (AN) from stripping-scrubbing, ammonium water (AW) and concentrate (CaE) from evaporation, and two tailor-made blends (AN + CaE and AW + CaE), for their potential as nitrogen (N) fertilizers in the pot cultivation of lettuce. Parallelly, a soil incubation experiment was conducted to investigate the N release dynamics of the tested RDFs. The RDFs were compared against the commercial calcium ammonium nitrate (CAN) and an unfertilized control. AN and AW fertilization resulted in a similar crop yield and N uptake to the CAN treatment. CaE and blends exhibited poor yield and N uptake, possibly due to the sodium toxicity detected. AN and AW displayed N fertilizer replacement values above 100%, whereas CaE and blends exhibited poor results in the current experiments. The soil incubation experiment showed a positive soil priming effect in AN and AW treatment, as their N release was over 100%. Further research under uncontrolled field conditions utilizing AN and AW for diverse crop types can validate their N replacement potential. Full article

One of the recent problems on waste sorting systems is their performance evaluation for proper decision making and management. For this purpose, multi-criteria methods can be used to evaluate the sorting system from both operational and financial perspectives. According to a recent literature review, there are no solutions for evaluating waste sorting systems that take into account: sorting point utilisation, sorting efficiency, waste stream irregularity, and technical system availability. In addition, the problem of data uncertainty and the need to use expert judgements indicate the need for the implementation of methods adjusted to the qualitative and quantitative assessment, such as the fuzzy approach. Following this, in order to overcome the presented limitations, the authors introduced the new assessment method for waste sorting systems based on multi-criteria model implementation and fuzzy theory use. Therefore, the developed model was based on a hierarchical fuzzy logic model for which appropriate membership function parameters and inference rules were defined. The specificity of the chosen assessment criteria and their justification was provided. The model has been implemented to evaluate one of the waste sorting plants in Wroclaw, Poland. Tests have been conducted for seven different configurations of waste sorting lines (with variable input parameters). The study focuses on analysing the amount of selected waste at each station in relation to the total stream size of each fraction. Efficiency was measured by the mass of the collected waste and the number of pieces of waste in each fraction. Based on the obtained results, estimations of particular parameters of the model were made, and the results were presented and commented on. It was shown that there is a significant relationship between the level of system evaluation and sorting efficiency and an inverse relationship with the level of RDF obtained. The analysis was based on Pearson’s linear correlation coefficient estimation and linear regression implementation. Full article

Phosphorus (P) is an essential plant nutrient routinely applied to soils as an agricultural fertiliser, frequently in non-renewable, inorganic forms. Finite reserves and growing demand for agricultural phosphorus mean alternative P resources need to be explored. Recycling-derived fertilisers (RDF) recovered from specific waste streams, using nutrient recovery technologies, have the potential to replace conventional phosphorus fertilisers used in agriculture. Healthy functioning soil microbial and nematode communities are essential players in maintaining soil health and nutrient status. Thus, it is important to assess the responses of these communities to RDF application. We compared soil microbial and nematode communities of conventional fertiliser and RDF treated soil, in the form of struvite and ash, using next-generation sequencing (NGS) technologies in a phosphate-fertiliser replacement value (P-FRV) field trial. Bacterial and nematode communities displayed significant changes under the different P fertilisation treatments, while fungal communities were relatively unaffected. Bacterial diversity was higher among RDF treatments than conventional treatments, while nematode diversity was reduced by one ash treatment. Available potassium and phosphate were the main drivers of bacterial community changes when analysed by canonical correspondence analysis (CCA), while available phosphate alone was the driver of nematode community shifts. Of the RDF, struvite products yielded the highest crop biomass, maintained microbial diversity and were associated with the least disturbed nematode communities. Full article

At most of the installations for the mechanical and biological treatment of waste operated in Poland, the 0–80 mm fraction, separated from the municipal waste stream, are completely stored after biostabilization. Such an action does not fit into the EU strategy focused on circular waste management. The purpose of this study was to assess the technical feasibility of recovering the mineral fractions contained in the compost-like-output (CLO) on a technological line designed for glass recovery. The research started in January 2019, lasted for the next 12 months, and covered 29 measurement series. The following two high-energy fractions were separated from the CLO: 10–35 mm light fraction after separation in the air separator (M-1) and 35–80 mm light fraction after separation in the air separator (M-2). The stabilization processing on the glass recovery line allowed for the recovery of two high energy fractions in the total amount of 24.5% of the processed, and it stabilized the product’s mass. In terms of materials, the M-1 and M-2 wastes were a mixture of organic, paper, and plastic materials. Under the Refuse Derived Fuel (RDF) classification, according to the European Committee for Standardization, the tested waste fell within the following classes: waste M-1: 4NCV2Cl4Hg and M-2: 4NCV1Cl4Hg. Full article

Digital rehabilitation is a novel concept that integrates state-of-the-art technologies for motion sensing and monitoring, with personalized patient-centric methodologies emerging from the field of physiotherapy. Thanks to the advances in wearable and portable sensing technologies, it is possible to provide patients with accurate monitoring devices, which simplifies the tracking of performance and effectiveness of physical exercises and treatments. Employing these approaches in everyday practice has enormous potential. Besides facilitating and improving the quality of care provided by physiotherapists, the usage of these technologies also promotes the personalization of treatments, thanks to data analytics and patient profiling (e.g., performance and behavior). However, achieving such goals implies tackling both technical and methodological challenges. In particular, (i) the capability of undertaking autonomous behaviors must comply with strict real-time constraints (e.g., scheduling, communication, and negotiation), (ii) plug-and-play sensors must seamlessly manage data and functional heterogeneity, and finally (iii) multi-device coordination must enable flexible and scalable sensor interactions. Beyond traditional top-down and best-effort solutions, unsuitable for safety-critical scenarios, we propose a novel approach for decentralized real-time compliant semantic agents. In particular, these agents can autonomously coordinate with each other, schedule sensing and data delivery tasks (complying with strict real-time constraints), while relying on ontology-based models to cope with data heterogeneity. Moreover, we present a model that represents sensors as autonomous agents able to schedule tasks and ensure interactions and negotiations compliant with strict timing constraints. Furthermore, to show the feasibility of the proposal, we present a practical study on upper and lower-limb digital rehabilitation scenarios, simulated on the MAXIM-GPRT environment for real-time compliance. Finally, we conduct an extensive evaluation of the implementation of the stream processing multi-agent architecture, which relies on existing RDF stream processing engines. Full article

We have been advancing the concept of carbonized refuse-derived fuel (CRDF) by refuse-derived fuel (RDF) torrefaction as improved recycling to synergistically address the world’s energy demand. The RDF is a combustible fraction of municipal solid waste (MSW). Many municipalities recover RDF for co-firing with conventional fuels. Torrefaction can further enhance fuel properties and valorize RDF. Energy demand for torrefaction is one of the key unknowns needed for scaling up CRDF production. To address this need, a pioneering model for optimizing site-specific energy demand for torrefaction of mixed RDF materials was developed. First, thermogravimetric and differential scanning calorimetry analyses were used to establish thermal properties for eight common RDF materials. Then, the model using the %RDF mix, empirical thermal properties, and torrefaction temperature was developed. The model results for individual RDF components fitted well (R² ≥ 0.98) with experimental torrefaction data. Finally, the model was used to find an optimized RDF site-specific mixture with the lowest energy demand. The developed model could be a basis for estimating a net energy potential from the torrefaction of mixed RDF. Improved models could be useful to make plant-specific decisions to optimize RDF production based on the energy demand that depends on highly variable types of MSW and RDF streams. Full article

Recently, many approaches have been proposed to manage sensor data using semantic web technologies for effective heterogeneous data integration. However, our empirical observations revealed that these solutions primarily focused on semantic relationships and unfortunately paid less attention to spatio–temporal correlations. Most semantic approaches do not have spatio–temporal support. Some of them have attempted to provide full spatio–temporal support, but have poor performance for complex spatio–temporal aggregate queries. In addition, while the volume of sensor data is rapidly growing, the challenge of querying and managing the massive volumes of data generated by sensing devices still remains unsolved. In this article, we introduce EAGLE, a spatio–temporal query engine for querying sensor data based on the linked data model. The ultimate goal of EAGLE is to provide an elastic and scalable system which allows fast searching and analysis with respect to the relationships of space, time and semantics in sensor data. We also extend SPARQL with a set of new query operators in order to support spatio–temporal computing in the linked sensor data context. Full article

Internet of Things (IoT) technologies have evolved rapidly during the last decade, and many architecture types have been proposed for distributed and interconnected systems. However, most systems are implemented following fragmented approaches for specific application domains, introducing difficulties in providing unified solutions. However, the unification of solutions is an important feature from an IoT perspective. In this paper, we present an IoT platform that supports multiple application layer communication protocols (Representational State Transfer (REST)/HyperText Transfer Protocol (HTTP), Message Queuing Telemetry Transport (MQTT), Advanced Message Queuing Protocol (AMQP), Constrained Application Protocol (CoAP), and Websockets) and that is composed of open-source frameworks (RabbitMQ, Ponte, OM2M, and RDF4J). We have explored a back-end system that interoperates with the various frameworks and offers a single approach for user-access control on IoT data streams and micro-services. The proposed platform is evaluated using its containerized version, being easily deployable on the vast majority of modern computing infrastructures. Its design promotes service reusability and follows a marketplace architecture, so that the creation of interoperable IoT ecosystems with active contributors is enabled. All the platform’s features are analyzed, and we discuss the results of experiments, with the multiple communication protocols being tested when used interchangeably for transferring data. Developing unified solutions using such a platform is of interest to users and developers as they can test and evaluate local instances or even complex applications composed of their own IoT resources before releasing a production version to the marketplace. Full article